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Abstract

To date, two main categories of OCT techniques have been described for imaging hemodynamics: Doppler OCT and OCT angiography. Doppler OCT can measure axial velocity profiles and flow in arteries and veins, while OCT angiography can determine vascular morphology, tone, and presence or absence of red blood cell (RBC) perfusion. However, neither method can quantify RBC velocity in capillaries, where RBC flow is typically transverse to the probe beam and single-file. Here, we describe new methods that potentially address these limitations. Firstly, we describe a complex-valued OCT signal in terms of a static scattering component, dynamic scattering component, and noise. Secondly, we propose that the time scale of random fluctuations in the dynamic scattering component are related to red blood cell velocity. Analysis was performed along the slow axis of repeated B-scans to parallelize measurements. We correlate our purported velocity measurements against two-photon microscopy measurements of RBC velocity, and investigate changes during hypercapnia. Finally, we image the ischemic stroke penumbra during distal middle cerebral artery occlusion (dMCAO), where OCT velocimetry methods provide additional insight that is not afforded by either Doppler OCT or OCT angiography.

Figures (7)

Frequency domain comparison of Doppler OCT (a), OCT angiography (b), and the proposed method of OCT velocimetry (c). (a) In Doppler OCT, the center frequency of the dynamic scattering component of the power spectral density is estimated, and used to determine the axial projection of velocity, vz. (b) In conventional OCT angiography, a high-pass filter is applied to suppress static scattering, and visualize only the dynamic scattering component. (c) In the method of OCT velocimetry presented here, the spectral width of the dynamic scattering component is estimated.

The autocorrelation function / power spectral density are superpositions of static (blue) and dynamic (red) scattering components. (a) Magnitude and phase of the autocorrelation function components. Due to coherent interference of the static and dynamic components, the autocorrelation magnitude exhibits oscillations (black line). (b) Power spectral density, obtained from Fourier transformation of the autocorrelation function, showing static and dynamic components. (c) En face image of total scattering from 250 to 300 µm depth in the rat somatosensory cortex. Static (d) and dynamic (e) scattering components can be estimated. Note that (c) and (d) are displayed on the same grayscale, while (e) is rescaled due to the lower dynamic scattering power. These components can be overlaid (f) to show the contributions of scattering from static tissue (blue) and dynamic red blood cells (red). For simplicity, noise sources have been neglected.

Examples of autocorrelation function estimates from vasculature. En face OCT angiogram of the rat somatosensory cortex through a cranial window. (b) OCT Δf map, showing bandwidth in color scale. Cross-sectional slice through the angiogram (c) and Δf map (d) are shown, along with 4 labeled points where the autocorrelation function is estimated. (e) Plot of autocorrelation function estimate magnitudes at the 4 labeled points. The Δf values shown in the legend are proportional to the rate of autocorrelation decay.

Comparison of OCT and two photon velocimetry. (a) OCT angiogram and (b) two-photon angiogram. The location of the two-photon angiogram is shown as a red box in (a). (c) OCT Δf map at the location of the red box in (b). with a capillary (above, black region-of-interest) and two-photon line scan (below, red arrow) at the same location. (d) The slope of the space-time plot from the line scan is inversely proportional to the velocity (e) Comparison of velocity measured by two-photon microscopy and Δf values measured by OCT at the same location. The black line shows a linear fit for capillaries and veins only.

Capillary velocimetry during a hypercapnic challenge. OCT angiograms under normocapnia (a) and hypercapnia (b) show no noticeable differences. However, OCT Δf map shows an apparent redistribution of capillary velocity during hypercapnia (d) compared to normocapnia (c). On average, the number of high-velocity capillaries is increased during hypercapnia.

OCT methods of velocimetry provide additional insight which is not obtained from conventional OCT angiograms. OCT angiograms of the mouse cortex before (a) and one day after (b) distal MCA occlusion are shown. Surface vessels (pial and dural) have been colored yellow, while deeper vessels are colored in green. Black and white OCT angiograms show a rarefaction of perfused capillaries after occlusion (d) compared to before occlusion (c). OCT Δf maps show a reduction in velocity in perfused capillaries after occlusion (f) compared to before occlusion (e). The Δf maps (e-f) provide novel information which is not contained in the angiograms (c-d), and show an increase in heterogeneity after stroke.